OpenRaiser/Intern-Atlas huggingface dataset:
Kinematic Analysis and Trajectory Planning of the Orthoglide 5-axis
Venue: arXiv cs.RO 2015
Year: 2015

The subject of this paper is about the kinematic analysis and the trajectory planning of the Orthoglide 5-axis. The Orthoglide 5-axis a five degrees of freedom parallel kinematic machine developed at IRCCyN and is made up of a hybrid architecture, namely, a three degrees of freedom translational parallel manip-ulator mounted in series with a two degrees of freedom parallel spherical wrist. The simpler the kinematic modeling of the Or-thoglide 5-axis, the higher the maximum frequency of its control loop. Indeed, the control loop of a parallel kinematic machine should be computed with a high frequency, i.e., higher than 1.5 MHz, in order the manipulator to be able to reach high speed motions with a good accuracy. Accordingly, the direct and inverse kinematic models of the Orthoglide 5-axis, its inverse kine-matic Jacobian matrix and the first derivative of the latter with respect to time are expressed in this paper. It appears that the kinematic model of the manipulator under study can be written in a quadratic form due to the hybrid architecture of the Orthoglide 5-axis. As illustrative examples, the profiles of the actuated joint angles (lengths), velocities and accelerations that are used in the control loop of the robot are traced for two test trajectories.
Linear Transmission of Composite Gaussian Measurements over a Fading Channel under Delay Constraints
Venue: arXiv cs.IT 2015
Year: 2015

Delay constrained linear transmission (LT) strategies are considered for the transmission of composite Gaussian measurements over an additive white Gaussian noise fading channel under an average power constraint. If the channel state information (CSI) is known by both the encoder and decoder, the optimal LT scheme in terms of the average mean-square error distortion is characterized under a strict delay constraint, and a graphical interpretation of the optimal power allocation strategy is presented. Then, for general delay constraints, two LT strategies are proposed based on the solution to a particular multiple measurements-parallel channels scenario. It is shown that the distortion decreases as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions. If the CSI is known only by the decoder, the optimal LT strategy is derived under a strict delay constraint. The extension for general delay constraints is shown to be hard. As a first step towards understanding the structure of the optimal scheme in this case, it is shown that for the multiple measurements-parallel channels scenario, any LT scheme that uses only a one-to-one linear mapping between measurements and channels is suboptimal in general.
Using Dimension Reduction to Improve the Classification of High-dimensional Data
Venue: arXiv cs.LG 2015
Year: 2015

In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods. We assessed two different dimension reduction variants: feature selection by ANOVA F-test and feature transformation by PCA. On the reduced datasets, we applied common learning algorithms using 5-fold cross-validation. Training, tuning of the hyperparameters, as well as the performance evaluation of the classifiers was conducted using two different performance measures: Accuracy, and Receiver Operating Characteristic curve (AUC). Our hypothesis is supported by experimental results.
Finding Influential Bloggers
Venue: arXiv cs.SI 2015
Year: 2015

Blogging is a popular way of expressing opinions and discussing topics. Bloggers demonstrate different levels of commitment and most interesting are influential bloggers. Around such bloggers, the groups are forming, which concentrate users sharing similar interests. Finding such bloggers is an important task and has many applications e.g. marketing, business, politics. Influential ones affect others which is related to the process of diffusion. However, there is no objective way to telling which blogger is more influential. Therefore, researchers take into consideration different criteria to assess bloggers (e.g. SNA centrality measures). In this paper we propose new, efficient method for influential bloggers discovery which is based on relation of commenting in blogger's thread and is defined on bloggers level. Next, we compare results with other, comparative method proposed by Agarwal et al. called iFinder which is based on links between posts.
Research on Solution Space of Bipartite Graph Vertex-Cover by Maximum Matchings
Venue: arXiv cs.DS 2015
Year: 2015

Some rigorous results and statistics of the solution space of Vertex-Covers on bipartite graphs are given in this paper. Based on the $K\ddot{o}nig$'s theorem, an exact solution space expression algorithm is proposed and statistical analysis of the nodes' states is provided. The statistical results fit well with the algorithmic results until the emergence of the unfrozen core, which makes the fluctuation of statistical quantities and causes the replica symmetric breaking in the solutions. Besides, the entropy of bipartite Vertex-Cover solutions is calculated with the clustering entropy using a cycle simplification technique for the unfrozen core. Furthermore, as generalization of bipartite graphs, bipartite core graph is proposed, the solution space of which can also be easily determined; and based on these results, how to generate a $K\ddot{o}nig-Egerv\acute{a}ry$ subgraph is studied by a growth process of adding edges. The investigation of solution space of bipartite graph Vertex-Cover provides intensive understanding and some insights on the solution space complexity, and will produce benefit for finding maximal $K\ddot{o}nig-Egerv\acute{a}ry$ subgraphs, solving general graph Vertex-Cover and recognizing the intrinsic hardness of NP-complete problems.
Smart Transformations: The Evolution of Choice Principles
Venue: arXiv cs.GT 2015
Year: 2015

Evolutionary game theory classically investigates which behavioral patterns are evolutionarily successful in a single game. More recently, a number of contributions have studied the evolution of preferences instead: which subjective conceptualizations of a game's payoffs give rise to evolutionarily successful behavior in a single game. Here, we want to extend this existing approach even further by asking: which general patterns of subjective conceptualizations of payoff functions are evolutionarily successful across a class of games. In other words, we will look at evolutionary competition of payoff transformations in "meta-games", obtained from averaging over payoffs of single games. Focusing for a start on the class of 2x2 symmetric games, we show that regret minimization can outperform payoff maximization if agents resort to a security strategy in case of radical uncertainty.
Interest-based RDF Update Propagation
Venue: arXiv cs.DC 2015
Year: 2015

Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and process large amounts of requests from diverse applications. Many data products and services rely on full or partial local LOD replications to ensure faster querying and processing. While such replicas enhance the flexibility of information sharing and integration infrastructures, they also introduce data duplication with all the associated undesirable consequences. Given the evolving nature of the original and authoritative datasets, to ensure consistent and up-to-date replicas frequent replacements are required at a great cost. In this paper, we introduce an approach for interest-based RDF update propagation, which propagates only interesting parts of updates from the source to the target dataset. Effectively, this enables remote applications to `subscribe' to relevant datasets and consistently reflect the necessary changes locally without the need to frequently replace the entire dataset (or a relevant subset). Our approach is based on a formal definition for graph-pattern-based interest expressions that is used to filter interesting parts of updates from the source. We implement the approach in the iRap framework and perform a comprehensive evaluation based on DBpedia Live updates, to confirm the validity and value of our approach.
Uplink Downlink Rate Balancing in Cooperating Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

Broadcast MIMO techniques can significantly increase the throughput in the downlink of cellular networks, at the price of channel state information (CSI) feedback from the mobiles, sent over the uplink. Thus, it creates a mechanism that can tradeoff some uplink capacity for increased downlink capacity. In this work we quantify this tradeoff and study the exchange ratio between the feedback rate (over the uplink) and the downlink rate. We study both finite and infinite networks, and show that for high enough (but finite) SNR, the uplink rate can be exchanged for increased downlink rate with a favorable exchange ratio. This exchange ratio is an increasing function of the channel coherence time, and a decreasing function of the number of measured base stations. We also show that devoting a constant fraction of the uplink to CSI feedback can increase the downlink multiplexing gain continuously from 0 to 1, in finite networks. On the other hand, in infinite networks (with infinite connectivity) our bounds can only show doubly logarithmic scaling of the rate with SNR. The presented results prove that the adaptation of the feedback rate can control the balance between the uplink and downlink rates. This capability is very important in modern cellular networks, where the operators need to respond to continuously changing user demands.
Relative Generalized Hamming Weights of Cyclic Codes
Venue: arXiv cs.IT 2015
Year: 2015

Relative generalized Hamming weights (RGHWs) of a linear code respect to a linear subcode determine the security of the linear ramp secret sharing scheme based on the code. They can be used to express the information leakage of the secret when some keepers of shares are corrupted. Cyclic codes are an interesting type of linear codes and have wide applications in communication and storage systems. In this paper, we investigate the RGHWs of cyclic codes with two nonzeros respect to any of its irreducible cyclic subcodes. Applying the method in the paper [arxiv.org/abs/1410.2702], we give two formulae for RGHWs of the cyclic codes. As applications of the formulae, explicit examples are computed.
Index Codes for the Gaussian Broadcast Channel using Quadrature Amplitude Modulation
Venue: arXiv cs.IT 2015
Year: 2015

We propose index codes, based on multidimensional QAM constellations, for the Gaussian broadcast channel, where every receiver demands all the messages from the source. The efficiency with which an index code exploits receiver side information in this broadcast channel is characterised by a code design metric called "side information gain". The known index codes for this broadcast channel enjoy large side information gains, but do not encode all the source messages at the same rate, and do not admit message sizes that are powers of two. The index codes proposed in this letter, which are based on linear codes over integer rings, overcome both these drawbacks and yet provide large values of side information gain. With the aid of a computer search, we obtain QAM index codes for encoding up to 5 messages with message sizes 2^m, m <= 6. We also present the simulated performance of a new 16-QAM index code, concatenated with an off-the-shelf LDPC code, which is observed to operate within 4.3 dB of the broadcast channel capacity.
Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis
Venue: arXiv cs.CL 2015
Year: 2015

This study analyzes political interactions in the European Parliament (EP) by considering how the political agenda of the plenary sessions has evolved over time and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making Parliamentary speeches. It does so by considering the context in which speeches are made, and the content of those speeches. To detect latent themes in legislative speeches over time, speech content is analyzed using a new dynamic topic modeling method, based on two layers of matrix factorization. This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999-2014. Our findings suggest that the political agenda of the EP has evolved significantly over time, is impacted upon by the committee structure of the Parliament, and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro-crisis have a significant impact on what is being discussed in Parliament.
Multidefender Security Games
Venue: arXiv cs.GT 2015
Year: 2015

Stackelberg security game models and associated computational tools have seen deployment in a number of high-consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. These models focus on isolated systems with only one defender, despite being part of a more complex system with multiple players. Furthermore, many real systems such as transportation networks and the power grid exhibit interdependencies between targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security, we investigate game theoretic models of security games with multiple defenders. Unlike most prior analysis, we focus on the situations in which each defender must protect multiple targets, so that even a single defender's best response decision is, in general, highly non-trivial. We start with an analytical investigation of multidefender security games with independent targets, offering an equilibrium and price-of-anarchy analysis of three models with increasing generality. In all models, we find that defenders have the incentive to over-protect targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Considering interdependencies among targets, we develop a novel mixed-integer linear programming formulation to compute a defender's best response, and make use of this formulation in approximating Nash equilibria of the game. We apply this approach towards computational strategic analysis of several models of networks representing interdependencies, including real-world power networks. Our analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or under-invest in security.
Optimized Password Recovery for Encrypted RAR on GPUs
Venue: arXiv cs.DC 2015
Year: 2015

RAR uses classic symmetric encryption algorithm SHA-1 hashing and AES algorithm for encryption, and the only method of password recovery is brute force, which is very time-consuming. In this paper, we present an approach using GPUs to speed up the password recovery process. However, because the major calculation and time-consuming part, SHA-1 hashing, is hard to be parallelized, so this paper adopts coarse granularity parallel. That is, one GPU thread is responsible for the validation of one password. We mainly use three optimization methods to optimize this parallel version: asynchronous parallel between CPU and GPU, redundant calculations and conditional statements reduction, and the usage of registers optimization. Experiment result shows that the final version reaches 43~57 times speedup on an AMD FirePro W8000 GPU, compared to a well-optimized serial version on Intel Core i5 CPU.
A Generative Model of Natural Texture Surrogates
Venue: arXiv cs.CV 2015
Year: 2015

Natural images can be viewed as patchworks of different textures, where the local image statistics is roughly stationary within a small neighborhood but otherwise varies from region to region. In order to model this variability, we first applied the parametric texture algorithm of Portilla and Simoncelli to image patches of 64X64 pixels in a large database of natural images such that each image patch is then described by 655 texture parameters which specify certain statistics, such as variances and covariances of wavelet coefficients or coefficient magnitudes within that patch. To model the statistics of these texture parameters, we then developed suitable nonlinear transformations of the parameters that allowed us to fit their joint statistics with a multivariate Gaussian distribution. We find that the first 200 principal components contain more than 99% of the variance and are sufficient to generate textures that are perceptually extremely close to those generated with all 655 components. We demonstrate the usefulness of the model in several ways: (1) We sample ensembles of texture patches that can be directly compared to samples of patches from the natural image database and can to a high degree reproduce their perceptual appearance. (2) We further developed an image compression algorithm which generates surprisingly accurate images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how our approach can be used for an efficient and objective evaluation of samples generated with probabilistic models of natural images.
A Category Theory of Communication Theory
Venue: arXiv cs.IT 2015
Year: 2015

A theory of how agents can come to understand a language is presented. If understanding a sentence $α$ is to associate an operator with $α$ that transforms the representational state of the agent as intended by the sender, then coming to know a language involves coming to know the operators that correspond to the meaning of any sentence. This involves a higher order operator that operates on the possible transformations that operate on the representational capacity of the agent. We formalize these constructs using concepts and diagrams analogous to category theory.
Performance-Complexity Analysis for MAC ML-based Decoding with User Selection
Venue: arXiv cs.IT 2015
Year: 2015

This work explores the rate-reliability-complexity limits of the quasi-static K-user multiple access channel (MAC), with or without feedback. Using high-SNR asymptotics, the work first derives bounds on the computational resources required to achieve near-optimal (ML-based) decoding performance. It then bounds the (reduced) complexity needed to achieve any (including suboptimal) diversity-multiplexing performance tradeoff (DMT) performance, and finally bounds the same complexity, in the presence of feedback-aided user selection. This latter effort reveals the ability of a few bits of feedback not only to improve performance, but also to reduce complexity. In this context, the analysis reveals the interesting finding that proper calibration of user selection can allow for near-optimal ML-based decoding, with complexity that need not scale exponentially in the total number of codeword bits. The derived bounds constitute the best known performance-vs-complexity behavior to date for ML-based MAC decoding, as well as a first exploration of the complexity-feedback-performance interdependencies in multiuser settings.
Linear Codes from a Generic Construction
Venue: arXiv cs.IT 2015
Year: 2015

A generic construction of linear codes over finite fields has recently received a lot of attention, and many one-weight, two-weight and three-weight codes with good error correcting capability have been produced with this generic approach. The first objective of this paper is to establish relationships among some classes of linear codes obtained with this approach, so that the parameters of some classes of linear codes can be derived from those of other classes with known parameters. In this way, linear codes with new parameters will be derived. The second is to present a class of three-weight binary codes and consider their applications in secret sharing.
Large Graph Analysis in the GMine System
Venue: arXiv cs.SI 2015
Year: 2015

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.
Estimating Visual Comfort in Stereoscopic Displays Using Electroencephalography: A Proof-of-Concept
Venue: arXiv cs.HC 2015
Year: 2015

With stereoscopic displays, a depth sensation that is too strong could impede visual comfort and result in fatigue or pain. Electroencephalography (EEG) is a technology which records brain activity. We used it to develop a novel brain-computer interface that monitors users' states in order to reduce visual strain. We present the first proof-of-concept system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. It reacts within 1s to depth variations, achieving 63% accuracy on average and 74% when 7 consecutive variations are measured. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions.
Unifying type systems for mobile processes
Venue: arXiv cs.LO 2015
Year: 2015

We present a unifying framework for type systems for process calculi. The core of the system provides an accurate correspondence between essentially functional processes and linear logic proofs; fragments of this system correspond to previously known connections between proofs and processes. We show how the addition of extra logical axioms can widen the class of typeable processes in exchange for the loss of some computational properties like lock-freeness or termination, allowing us to see various well studied systems (like i/o types, linearity, control) as instances of a general pattern. This suggests unified methods for extending existing type systems with new features while staying in a well structured environment and constitutes a step towards the study of denotational semantics of processes using proof-theoretical methods.
SEPIA: Search for Proofs Using Inferred Automata
Venue: arXiv cs.LO 2015
Year: 2015

This paper describes SEPIA, a tool for automated proof generation in Coq. SEPIA combines model inference with interactive theorem proving. Existing proof corpora are modelled using state-based models inferred from tactic sequences. These can then be traversed automatically to identify proofs. The SEPIA system is described and its performance evaluated on three Coq datasets. Our results show that SEPIA provides a useful complement to existing automated tactics in Coq.